{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be58b722",
   "metadata": {},
   "source": [
    "# Денситометр"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "93334688",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -5.44006\n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "      99        227.15   5.03588e-05        59.497      0.2141      0.8404      126   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     174         227.2    0.00021713       88.3739   3.461e-06       0.001      296  LS failed, Hessian reset \n",
      "     199       227.232   5.33924e-05       86.0059           1           1      327   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     259       227.234   6.46222e-09       99.9821     0.01189           1      410   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: absolute parameter change was below tolerance\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2106.39095752, 1971.14496295, 1873.26313368, 1894.25230106])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from fbprophet import Prophet\n",
    "\n",
    "denc = pd.read_excel('Data/Denc.xlsx')\n",
    "\n",
    "denc['ds'] = pd.to_datetime(denc['Год'].astype(str) + '-W' + denc['Номер недели'].astype(str) + '-0', format='%Y-W%W-%w')\n",
    "denc['y'] = denc['Денситометр'].astype(float)\n",
    "\n",
    "denc = denc.drop(['Год', 'Номер недели', 'Денситометр'],axis=1)\n",
    "\n",
    "model = Prophet(\n",
    "#     growth='linear',\n",
    "#     changepoints=None,\n",
    "#     n_changepoints=25,\n",
    "#     changepoint_range=0.8,\n",
    "#     yearly_seasonality='auto',\n",
    "#     weekly_seasonality='auto',\n",
    "#     daily_seasonality='auto',\n",
    "#     holidays=None,\n",
    "#     seasonality_mode='additive',\n",
    "#     seasonality_prior_scale=10.0,\n",
    "#     holidays_prior_scale=10.0,\n",
    "#     changepoint_prior_scale=0.05,\n",
    "#     mcmc_samples=0,\n",
    "#     interval_width=0.8,\n",
    "#     uncertainty_samples=1000,\n",
    "#     stan_backend=None,\n",
    ")\n",
    "\n",
    "model.add_country_holidays(country_name='RU')\n",
    "data = denc[['ds', 'y']]\n",
    "model.fit(data)\n",
    "future = model.make_future_dataframe(periods=4, freq='W')\n",
    "\n",
    "forecast = model.predict(future)\n",
    "\n",
    "pred = forecast[['ds', 'yhat']].tail(4)\n",
    "pred['yhat'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3ce155d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/simon/anaconda3/lib/python3.9/site-packages/fbprophet/plot.py:70: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "  fcst_t = fcst['ds'].dt.to_pydatetime()\n",
      "/home/simon/anaconda3/lib/python3.9/site-packages/fbprophet/plot.py:71: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "  ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043ba318",
   "metadata": {},
   "source": [
    "Это можно оставить, так как тут удобно добавлять праздники и прочие change_point, но в MVP засунем LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "9293b3f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.98265362\n",
      "epoch: 1 test: 0.48937808863806476\n",
      "epoch:  51 loss: 0.98155117\n",
      "epoch: 51 test: 0.49023191631651475\n",
      "epoch: 101 loss: 0.98311949\n",
      "epoch: 101 test: 0.4921908889961554\n",
      "epoch: 151 loss: 0.99339736\n",
      "epoch: 151 test: 0.49580010345076897\n",
      "epoch: 201 loss: 0.99346453\n",
      "epoch: 201 test: 0.49546844698387316\n",
      "epoch: 251 loss: 0.99369186\n",
      "epoch: 251 test: 0.49527251649045656\n",
      "epoch: 301 loss: 0.98577887\n",
      "epoch: 301 test: 0.4922135819802157\n",
      "epoch: 351 loss: 0.98650104\n",
      "epoch: 351 test: 0.4925879663790317\n",
      "epoch: 401 loss: 0.98668170\n",
      "epoch: 401 test: 0.491992533044706\n",
      "epoch: 451 loss: 0.99336904\n",
      "epoch: 451 test: 0.49596480201883164\n",
      "epoch: 501 loss: 0.99085718\n",
      "epoch: 501 test: 0.4967863053670336\n",
      "epoch: 551 loss: 0.98165786\n",
      "epoch: 551 test: 0.49975198624692285\n",
      "epoch: 601 loss: 0.90679282\n",
      "epoch: 601 test: 0.5008623001688385\n",
      "epoch: 651 loss: 0.89408839\n",
      "epoch: 651 test: 0.38756261004269865\n",
      "epoch: 701 loss: 0.86493587\n",
      "epoch: 701 test: 0.23246134511868022\n",
      "epoch: 751 loss: 0.85103440\n",
      "epoch: 751 test: 0.13405049821336712\n",
      "epoch: 801 loss: 0.82254606\n",
      "epoch: 801 test: 0.14102018052253174\n",
      "epoch: 851 loss: 0.68393028\n",
      "epoch: 851 test: 0.12745047306668922\n",
      "epoch: 901 loss: 0.73879236\n",
      "epoch: 901 test: 0.17078839480130648\n",
      "epoch: 951 loss: 0.48281866\n",
      "epoch: 951 test: 0.19851071560713796\n",
      "epoch: 1001 loss: 0.48558110\n",
      "epoch: 1001 test: 0.23521593620287828\n",
      "epoch: 1051 loss: 0.52533102\n",
      "epoch: 1051 test: 0.3130937238229965\n",
      "epoch: 1101 loss: 0.48352683\n",
      "epoch: 1101 test: 0.191548485954953\n",
      "epoch: 1151 loss: 0.46725595\n",
      "epoch: 1151 test: 0.15987954843154595\n",
      "epoch: 1201 loss: 0.37580925\n",
      "epoch: 1201 test: 0.13897069968033515\n",
      "epoch: 1251 loss: 0.45887238\n",
      "epoch: 1251 test: 0.3292853497253454\n",
      "epoch: 1301 loss: 0.19594681\n",
      "epoch: 1301 test: 0.10536096663207672\n",
      "epoch: 1351 loss: 0.10175020\n",
      "epoch: 1351 test: 0.09208779706212578\n",
      "0.07322180271148682\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/Denc.xlsx')[:67]\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_Денситометр'] = scaler.fit_transform(\n",
    "    denc['Денситометр'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "future = 4\n",
    "train_data = denc['scaled_Денситометр'].values[:-future]\n",
    "test_data = denc['Денситометр'].values[-future:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 3000\n",
    "loss_list = []\n",
    "loss_test = []\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    loss_list.append(single_loss.item())\n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "\n",
    "        seq = torch.from_numpy(train_data).float()\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for f in range(future):\n",
    "                pred = model(seq[-seq_length:])\n",
    "                seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "        predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "        tst = abs(1 - predicted_data[-future:].ravel()/test_data)\n",
    "\n",
    "        print(f'epoch: {i} test: {tst.max()}')\n",
    "        loss_test.append(tst.max())\n",
    "        model.train()\n",
    "\n",
    "    if single_loss.item() < 0.1:\n",
    "        print(single_loss.item())\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d826ebb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# denc['Денситометр'][-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "018cf948",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(loss_list[::50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "bae118ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_losses(train_loss_values, test_loss_values, epoch):\n",
    "    x = np.arange(1, epoch + 1)\n",
    "    plt.figure(figsize=(8, 6))  # Увеличим размер графика для лучшей видимости\n",
    "    plt.plot(x, train_loss_values, marker='o', linestyle='-', color='b', label='Train Loss')  # Укажем стиль и цвет линии для train loss\n",
    "    plt.plot(x, test_loss_values, marker='s', linestyle='-', color='r', label='Test Loss')  # Укажем стиль и цвет линии для test loss\n",
    "    plt.title('Настройка LSTM для Денситометр')  # Заголовок графика\n",
    "    plt.xlabel('Эпоха')  # Название оси X\n",
    "    plt.ylabel('|1 - predict/target|')  # Название оси Y\n",
    "    plt.grid(True)  # Включим сетку на графике\n",
    "    plt.legend()  # Включим легенду\n",
    "    plt.tight_layout()  # Улучшим компактность отображения\n",
    "    plt.show()\n",
    "    \n",
    "plot_losses(loss_list[::50], loss_test, len(loss_list[::50]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58f94d81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3391275d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/dens_lstm_1.pt\" \n",
    "# PATH = \"models/dens_lstm_3.pt\" # тут 5% лосс\n",
    "# torch.save(model.state_dict(), PATH)\n",
    "model = LSTM()\n",
    "model.load_state_dict(torch.load(PATH))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a657b3",
   "metadata": {},
   "source": [
    "### инференс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c4bdad58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# выгружаем данные о количестве исследований в прошлом\n",
    "# модель смотрит 6 последних недель seq_length = 6\n",
    "# и делает предсказание на 1 неделю вперед,\n",
    "# добаввляет в последдовательность и идет дальше\n",
    "denc = pd.read_excel('Data/Denc.xlsx')  \n",
    "\n",
    "denc['scaled_Денситометр'] = scaler.fit_transform(\n",
    "    denc['Денситометр'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "data = denc['scaled_Денситометр'].values\n",
    "\n",
    "future = 4 # прогноз на 4 недели\n",
    "\n",
    "seq = torch.from_numpy(data).float()\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for f in range(future):\n",
    "        pred = model(seq[-seq_length:]) # на неделю вперед\n",
    "        seq = torch.cat((seq, pred.view(-1))) # сую в input\n",
    "\n",
    "# для каждой позиции свой  scaler\n",
    "predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "predicted_data[-future:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c2779e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1436.215 ],\n",
       "       [1631.5752],\n",
       "       [1410.2308],\n",
       "       [1420.6974]], dtype=float32)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "fba65eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112, 1)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cd042a",
   "metadata": {},
   "source": [
    "# KT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "34f3161a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.10377419\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/KT.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_КТ'] = scaler.fit_transform(\n",
    "    denc['КТ'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_КТ'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c00092c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/kt_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e049bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "future = 4 # прогноз на 4 недели\n",
    "seq = torch.from_numpy(train_data).float()\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for f in range(future):\n",
    "        pred = model(seq[-seq_length:]) # на неделю вперед\n",
    "        seq = torch.cat((seq, pred.view(-1))) # сую в input\n",
    "\n",
    "# для каждой позиции свой  scaler\n",
    "predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "predicted_data[-future:] # тут сразу весь временной ряд"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879eb473",
   "metadata": {},
   "source": [
    "# КТ с КУ 1 зона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "99d1fbe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.40038872\n",
      "epoch:  51 loss: 0.33739144\n",
      "epoch: 101 loss: 0.19033355\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/KT1.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_КТ с КУ 1 зона'] = scaler.fit_transform(\n",
    "    denc['КТ с КУ 1 зона'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_КТ с КУ 1 зона'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    \"\"\"\n",
    "    мб добавить в тарегт + 1, чтобы избежать деления на 0\n",
    "    \"\"\"\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3dc93e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/kt1_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e248521c",
   "metadata": {},
   "source": [
    "# КТ с КУ 2 и более зон"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "11b70946",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 1.03438473\n",
      "epoch:  51 loss: 0.80628026\n",
      "epoch: 101 loss: 0.67001855\n",
      "epoch: 151 loss: 0.52770513\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/KT2.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_КТ с КУ 2 и более зон'] = scaler.fit_transform(\n",
    "    denc['КТ с КУ 2 и более зон'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_КТ с КУ 2 и более зон'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    \"\"\"\n",
    "    мб добавить в тарегт + 1, чтобы избежать деления на 0\n",
    "    \"\"\"\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cf71903f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/kt2_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd5a81e",
   "metadata": {},
   "source": [
    "# ММГ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1eae0fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.89918488\n",
      "epoch:  51 loss: 0.92307210\n",
      "epoch: 101 loss: 0.65777647\n",
      "epoch: 151 loss: 0.59965277\n",
      "epoch: 201 loss: 0.55438066\n",
      "epoch: 251 loss: 0.46549338\n",
      "epoch: 301 loss: 0.48275453\n",
      "epoch: 351 loss: 0.40552890\n",
      "epoch: 401 loss: 0.29440713\n",
      "epoch: 451 loss: 0.22840440\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/MMG.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_ММГ'] = scaler.fit_transform(\n",
    "    denc['ММГ'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_ММГ'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    \"\"\"\n",
    "    мб добавить в тарегт + 1, чтобы избежать деления на 0\n",
    "    \"\"\"\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "24511e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/mmg_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aff0d30",
   "metadata": {},
   "source": [
    "# МРТ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "34777467",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.44974196\n",
      "epoch:  51 loss: 0.19233292\n",
      "epoch: 101 loss: 0.11924243\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/MRT.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_МРТ'] = scaler.fit_transform(\n",
    "    denc['МРТ'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_МРТ'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0cdb87e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/mrt_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcd6191",
   "metadata": {},
   "source": [
    "# МРТ с КУ 1 зона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "65097915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.37661010\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/MRT_1.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_МРТ с КУ 1 зона'] = scaler.fit_transform(\n",
    "    denc['МРТ с КУ 1 зона'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_МРТ с КУ 1 зона'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    \"\"\"\n",
    "    мб добавить в тарегт + 1, чтобы избежать деления на 0\n",
    "    \"\"\"\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6054af4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/mrt_1_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aad411c",
   "metadata": {},
   "source": [
    "# МРТ с КУ 2 и более зон"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef2a9a8",
   "metadata": {},
   "source": [
    "эта отлиается от остальныхЮ, так как тут нули есть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5ab9f2e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.63575476\n",
      "epoch:  51 loss: 0.46020734\n",
      "epoch: 101 loss: 0.39451563\n",
      "epoch: 151 loss: 0.33025032\n",
      "epoch: 201 loss: 0.25951457\n",
      "epoch: 251 loss: 0.17598313\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/MRT_2.xlsx')\n",
    "# scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "# denc['scaled_МРТ с КУ 2 и более зон'] = scaler.fit_transform(\n",
    "#     denc['МРТ с КУ 2 и более зон'].values.reshape(-1, 1)\n",
    "# )\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['МРТ с КУ 2 и более зон'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.ones(1,1,self.hidden_layer_size),\n",
    "                            torch.ones(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    \"\"\"\n",
    "    тут встречаются нули\n",
    "    \"\"\"\n",
    "    loss = torch.max((torch.abs(1 - output/(target+1))))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "model.train()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 1000\n",
    "model.train()\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "#         print(y_pred)\n",
    "        \n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        \n",
    "        single_loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "#         print(single_loss, y_pred, leabels)\n",
    "#         break\n",
    "    \n",
    "    if i%50==1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n",
    "#     break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d040f279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/mrt_2_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249512b1",
   "metadata": {},
   "source": [
    "# РГ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8e11bd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.96913952\n",
      "epoch:  51 loss: 0.88768405\n",
      "epoch: 101 loss: 0.82981730\n",
      "epoch: 151 loss: 0.74225092\n",
      "epoch: 201 loss: 0.61373210\n",
      "epoch: 251 loss: 0.38339180\n",
      "epoch: 301 loss: 0.34376258\n",
      "epoch: 351 loss: 0.29203886\n",
      "epoch: 401 loss: 0.14838052\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/rg.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_РГ'] = scaler.fit_transform(\n",
    "    denc['РГ'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_РГ'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aaac4d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/rg_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9930ee3",
   "metadata": {},
   "source": [
    "# Флюорограф"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da51882b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 1.02436543\n",
      "epoch:  51 loss: 0.96470320\n",
      "epoch: 101 loss: 0.87205923\n",
      "epoch: 151 loss: 0.30717081\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "denc = pd.read_excel('Data/flur.xlsx')\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "denc['scaled_Флюорограф'] = scaler.fit_transform(\n",
    "    denc['Флюорограф'].values.reshape(-1, 1)\n",
    ")\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    для тренировки LSTM\n",
    "    \"\"\"\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data)-seq_length):\n",
    "        x = data[i:i+seq_length] # 3 недели на вход\n",
    "        y = data[i+seq_length:i+seq_length+1] # на след неделю\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "seq_length = 6 # мы подаем на вход это количество недель\n",
    "train_data = denc['scaled_Флюорограф'].values\n",
    "# test_data = denc['scaled_Денситометр'].values[-seq_length:]\n",
    "\n",
    "# Создание последовательностей\n",
    "X_train, y_train = create_sequences(train_data, seq_length)\n",
    "X_train = torch.from_numpy(X_train).float()\n",
    "y_train = torch.from_numpy(y_train).float()\n",
    "\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
    "                            torch.zeros(1,1,self.hidden_layer_size))\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(\n",
    "            input_seq.view(len(input_seq) ,1, -1),\n",
    "            self.hidden_cell\n",
    "        )\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]\n",
    "    \n",
    "def my_loss(output, target):\n",
    "    loss = torch.max((torch.abs(1 - output/target)))\n",
    "    return loss\n",
    "\n",
    "model = LSTM()\n",
    "loss_function = my_loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "lambda1 = lambda epoch: 0.99 ** epoch/2\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n",
    "\n",
    "epochs = 2000\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in zip(X_train, y_train):\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if i%50 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "        # не тестируем, обучаем на всех данных\n",
    "#         future = 4\n",
    "#         seq = torch.from_numpy(test_data).float()\n",
    "#         model.eval()\n",
    "#         with torch.no_grad():\n",
    "#             for f in range(future):\n",
    "#                 pred = model(seq[-seq_length:])\n",
    "#                 seq = torch.cat((seq, pred.view(-1)))\n",
    "\n",
    "#         predicted_data = scaler.inverse_transform(seq.numpy().reshape(-1, 1))\n",
    "#         tst = abs(1 - predicted_data[-future:].ravel()/denc_test['Денситометр'].to_numpy())\n",
    "# #         print()\n",
    "#         print(f'epoch: {i} test: {tst.max()}')\n",
    "#         model.train()\n",
    "    if single_loss.item() < 0.1 and i > 2:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "724ffd33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"models/flur_lstm_1.pt\" \n",
    "torch.save(model.state_dict(), PATH)\n",
    "# model = LSTM()\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1200a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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